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              仿生嗅覺感知系統氣體識別和濃度估計模型

              相洪濤 張文文 肖文鑫 王磊 王遠西

              相洪濤, 張文文, 肖文鑫, 王磊, 王遠西. 仿生嗅覺感知系統氣體識別和濃度估計模型. 自動化學報, 2024, 50(4): 812?827 doi: 10.16383/j.aas.c220689
              引用本文: 相洪濤, 張文文, 肖文鑫, 王磊, 王遠西. 仿生嗅覺感知系統氣體識別和濃度估計模型. 自動化學報, 2024, 50(4): 812?827 doi: 10.16383/j.aas.c220689
              Xiang Hong-Tao, Zhang Wen-Wen, Xiao Wen-Xin, Wang Lei, Wang Yuan-Xi. Gas recognition and concentration estimation model for bionic olfactory perception system. Acta Automatica Sinica, 2024, 50(4): 812?827 doi: 10.16383/j.aas.c220689
              Citation: Xiang Hong-Tao, Zhang Wen-Wen, Xiao Wen-Xin, Wang Lei, Wang Yuan-Xi. Gas recognition and concentration estimation model for bionic olfactory perception system. Acta Automatica Sinica, 2024, 50(4): 812?827 doi: 10.16383/j.aas.c220689

              仿生嗅覺感知系統氣體識別和濃度估計模型

              doi: 10.16383/j.aas.c220689
              基金項目: 國家自然科學基金(62203307)資助
              詳細信息
                作者簡介:

                相洪濤:山西大學自動化與軟件學院碩士研究生. 主要研究方向為信號處理, 深度學習. E-mail: xianghongtao0921@163.com

                張文文:南洋理工大學電氣與電子工程學院博士后研究員. 2021年獲得同濟大學博士學位. 主要研究方向為智能信息處理與模式識別算法, 仿生傳感器檢測技術與測量系統, 信號處理和深度學習. 本文通信作者. E-mail: wenwen.zhang@ntu.edu.sg

                肖文鑫:北京大學計算機學院博士研究生. 主要研究方向為軟件工程, 機器學習. E-mail: wenxin.xiao@stu.pku.edu.cn

                王磊:同濟大學電子與信息工程學院教授. 主要研究方向為傳感器檢測技術與測量系統. E-mail: leiwang@#edu.cn

                王遠西:同濟大學電子與信息工程學院博士研究生. 主要研究方向為傳感器檢測技術與測量系統. E-mail: 2010146@#edu.cn

              Gas Recognition and Concentration Estimation Model for Bionic Olfactory Perception System

              Funds: Supported by National Natural Science Foundation of China (62203307)
              More Information
                Author Bio:

                XIANG Hong-Tao Master student at the School of Automation and Software Engineering, Shanxi University. His research interest covers signal processing and deep learning

                ZHANG Wen-Wen Research fellow at the School of Electrical and Electronic Engineering, Nanyang Technological University. He received his Ph.D. degree from Tongji University in 2021. His research interest covers intelligent information processing and pattern recognition algorithms, bionic sensor detection technology and measurement systems, signal processing, and deep learning. Corresponding author of this paper

                XIAO Wen-Xin Ph.D. candidate at the School of Computer Science, Peking University. His research interest covers software engineering and machine learning

                WANG Lei Professor at the College of Electronic and Information Engineering, Tongji University. His research interest covers sensor detection technology and measurement system

                WANG Yuan-Xi Ph.D. candidate at the College of Electronic and Information Engineering, Tongji University. His research interest covers sensor detection technology and measurement system

              • 摘要: 常用氣體檢測模型需要使用氣體傳感器陣列響應信號的穩態值對氣體進行種類識別和濃度估計, 而在實際環境 中, 氣體一般處于動態變化的狀態, 氣體傳感器陣列響應信號難以達到穩態值或長時間維持穩定狀態. 針對上述問題, 提出 一種由動態小波殘差卷積神經網絡(Dynamic wavelet residual convolutional neural network, DWRCNN)子模型和權重 信號自注意力(Weighted signal self-attention, WSSA)子模型組成的氣體檢測模型. 該模型可以直接使用氣體傳感器陣列 的原始動態響應信號對動態變化的氣體進行成分識別, 并進一步對每種成分氣體的濃度在線估計. 通過搭建的仿生嗅覺感 知系統對模型的性能進行評估, 實驗結果表明, 與常用氣體識別模型相比, DWRCNN能獲得接近 100%氣體識別準確率, 且在線訓練時間短, 收斂速度快; 與常用氣體濃度估計模型相比, WSSA濃度估計模型能夠大幅提高氣體濃度估計精度, 并 能同時對不同氣體都保持較高氣體濃度估計精度, 解決了動態環境中仿生嗅覺感知系統需要針對不同氣體選擇不同最優氣 體濃度估計模型問題.
              • 圖  1  DWRCNN-WSSA模型氣體檢測流程

                Fig.  1  Flow of gas detection by DWRCNN-WSSA model

                圖  2  實驗裝置和平臺

                Fig.  2  Experimental setup and platform

                圖  3  當CO濃度為140 ppm時, CO傳感器陣列的動態響應信號曲線

                Fig.  3  Dynamic response signal curve of the sensor array for 140 ppm CO

                圖  4  傳感器陣列動態響應信號小波分解過程

                Fig.  4  Wavelet decomposition process of dynamic response signal of sensor array

                圖  5  TGS2610在140 ppm CO下的動態響應信號曲線和相應的5層低頻小波系數曲線

                Fig.  5  Dynamic response signal curve and corresponding 5-layer low-frequency wavelet coefficient curve at 140 ppm CO for TGS2610

                圖  6  傳感器陣列動態響應信號轉換為小波系數圖像過程

                Fig.  6  Process of converting the dynamic response signal of the sensor array into a wavelet coefficient map

                圖  7  DWRCNN氣體識別模型結構

                Fig.  7  Structure of the DWRCNN gas recognition model

                圖  8  計算復雜度圖解

                Fig.  8  Illustration of computational complexity

                圖  9  WSSA氣體濃度估計模型的結構

                Fig.  9  Structure of the WSSA gas concentration estimation model

                圖  10  不同模型的氣體識別結果混淆矩陣

                Fig.  10  Confusion matrix of gas recognition results with different models

                圖  11  不同模型氣體識別的準確率曲線和損失函數曲線

                Fig.  11  Accuracy curve and loss function curve of gas recognition with different models

                圖  12  PCA降維可視化

                Fig.  12  Visualization of PCA dimensionality reduction

                圖  13  不同模型的氣體濃度估計誤差箱式圖

                Fig.  13  Error box plots of gas concentration estimation with different models

                圖  14  SA模型和WSSA模型的氣體濃度估計散點圖

                Fig.  14  Scatter plots of gas concentration estimation for SA model and WSSA model

                圖  15  氣腔進氣口位置和傳感器陣列高度示意圖

                Fig.  15  Diagram of gas cavity inlet position and sensor array height

                表  1  氣體傳感器陣列詳細信息

                Table  1  Gas sensor array details

                通道編號傳感器型號公司名稱 敏感的主要氣體種類
                通道0MQ135WinsenNH3、H2S、C6H6
                通道1TGS813FIGAROCH4、CH3CH2CH3
                通道2TGS2611FIGAROCH4
                通道3TGS2610FIGAROCH3CH2CH3、C4H10
                通道4TGS2620FIGAROC2H6O、有機溶劑
                通道5TGS2600FIGARO H2、C2H6O
                通道6TGS2602FIGAROVOC、NH3、H2S、CH2O
                通道7MP503WinsenC2H6O、C4H10、CH2O
                下載: 導出CSV

                表  2  不同模型的氣體識別準確率 (%)

                Table  2  Gas recognition accuracy of different models (%)

                方法KNNSVMRFNB
                準確率95.7496.4595.7492.91
                方法BPNNCNNCapsNetDWRCNN
                準確率97.8799.29100.00100.00
                下載: 導出CSV

                表  3  CO濃度估計指標

                Table  3  Metrics of CO concentration estimation

                方法MAERMSEEV${\rm{R}}^2$
                BR6.5528.0940.9430.942
                SVM4.2587.0150.9630.957
                DT5.4728.0390.9490.943
                KNN5.0337.0750.9580.956
                RF4.7137.0740.9590.956
                Adaboost5.4777.6430.9500.949
                GBDT4.8177.0190.9600.957
                Bagging4.7607.0610.9590.956
                XGBoost4.6727.0350.9610.960
                OSA3.6304.2290.9870.986
                LSTM2.9343.8450.9880.988
                WS-LSTM2.3503.0350.9930.993
                SA2.9163.7560.9890.989
                WSSA2.0902.6460.9950.994
                下載: 導出CSV

                表  4  H2濃度估計指標

                Table  4  Metrics of H2 concentration estimation

                方法MAERMSEEV${\rm{R}}^2$
                BR16.09718.2840.6830.638
                SVM5.0346.9760.9550.947
                DT5.2068.9870.9210.913
                KNN6.3128.8650.9310.915
                RF5.0737.1570.9510.945
                Adaboost5.4417.2090.9520.944
                GBDT5.6878.4440.9310.923
                Bagging5.3467.6670.9400.936
                XGBoost5.5128.7240.9370.935
                OSA4.1555.3430.9770.977
                LSTM4.2645.3050.9740.973
                WS-LSTM3.7814.4570.9850.984
                SA4.1565.5600.9750.975
                WSSA2.3603.0280.9930.992
                下載: 導出CSV

                表  5  混合氣體中CO濃度估計指標

                Table  5  Metrics of CO concentration estimation in the gas mixture

                方法MAERMSEEVR2
                BR20.13424.4870.5300.526
                SVM20.00924.6570.5190.519
                DT20.53725.7460.5150.476
                KNN21.23626.7010.4430.436
                RF18.52923.6140.5810.559
                Adaboost19.95025.0190.5260.505
                GBDT20.83026.1930.4810.457
                Bagging19.60825.2880.5310.494
                XGBoost15.93120.1010.6190.592
                OSA10.90913.5890.8600.859
                LSTM10.43914.0500.8560.849
                WS-LSTM 7.95811.1880.9110.904
                SA 9.20912.9580.8720.872
                WSSA 6.014 7.6160.9560.956
                下載: 導出CSV

                表  6  混合氣體中H2濃度估計指標

                Table  6  Metrics of H2 concentration estimation in the gas mixture

                方法MAERMSEEVR2
                BR9.95612.3780.8970.897
                SVM8.00810.1060.9310.931
                DT11.32615.5030.8420.838
                KNN7.2979.6410.9370.937
                RF7.85210.8230.9220.921
                Adaboost9.12011.5820.9150.909
                GBDT7.76310.6220.9240.924
                Bagging8.01911.3940.9150.912
                XGBoost7.84010.0890.9320.931
                OSA8.88611.7200.9120.910
                LSTM7.7838.8480.9490.949
                WS-LSTM5.0956.8780.9690.969
                SA5.9067.7760.9600.960
                WSSA4.3186.3620.9740.973
                下載: 導出CSV

                表  7  氣體識別準確率 (%)

                Table  7  Gas recognition accuracy (%)

                氣腔進氣口位置ABC
                準確率100100100
                傳感器陣列擺放高度EFG
                準確率100100100
                下載: 導出CSV

                表  8  氣腔進氣口位置不同時單一氣體濃度估計的指標

                Table  8  Metrics for concentration estimation of single gas with different gas cavity inlet positions

                進氣口位置氣體種類MAERMSEEVR2
                ACO2.0902.6460.9950.994
                H22.3603.0280.9930.992
                BCO2.3263.0170.9940.994
                H22.2872.8980.9940.994
                CCO2.1852.8120.9950.994
                H22.4193.1770.9920.992
                下載: 導出CSV

                表  9  氣腔進氣口位置不同時混合氣體濃度估計的指標

                Table  9  Metrics for concentration estimation of mixed gases with different gas cavity inlet positions

                進氣口位置氣體種類MAERMSEEVR2
                ACO6.0147.6160.9560.956
                H24.3186.3620.9740.973
                BCO5.6796.8990.9630.962
                H24.5626.7130.9730.973
                CCO5.8787.2560.9610.961
                H24.7856.8960.9720.972
                下載: 導出CSV

                表  10  傳感器陣列擺放高度不同時單一氣體濃度估計指標

                Table  10  Metrics for concentration estimation of single gas with different sensor array placement heights

                高度氣體種類MAERMSEEVR2
                ECO2.0902.6460.9950.994
                H22.3603.0280.9930.992
                FCO2.2832.8780.9940.994
                H22.2262.8730.9940.994
                GCO2.3753.1220.9930.993
                H22.4513.1630.9920.992
                下載: 導出CSV

                表  11  傳感器陣列擺放高度不同時混合氣體濃度估計指標

                Table  11  Metrics for concentration estimation of mixed gases with different sensor array placement heights

                高度氣體種類MAERMSEEVR2
                ECO6.0147.6160.9560.956
                H24.3186.3620.9740.973
                FCO6.3238.0120.9550.955
                H24.6196.9920.9720.972
                GCO6.2257.8960.9560.955
                H24.6737.1050.9720.972
                下載: 導出CSV

                表  12  本文模型的氣體識別準確率 (%)

                Table  12  Gas recognition accuracy of our model (%)

                信號采集第1次第2次第3次
                準確率100.00100.0099.29
                下載: 導出CSV

                表  13  單一氣體濃度估計指標

                Table  13  Metrics for concentration estimation of single gas

                信號采集氣體種類MAERMSEEVR2
                第1次CO2.0902.6460.9950.994
                H22.3603.0280.9930.992
                第2次CO2.5123.2830.9920.992
                H22.8143.6840.9910.991
                第3次CO2.9853.8720.9890.989
                H23.3504.1150.9880.987
                下載: 導出CSV

                表  14  混合氣體濃度估計指標

                Table  14  Metrics for concentration estimation of mixed gases

                信號采集氣體種類MAERMSEEVR2
                第1次CO6.0147.6160.9560.956
                H24.3186.3620.9740.973
                第2次CO6.7118.8130.9410.940
                H25.1576.9720.9670.967
                第3次CO7.0169.4370.9340.934
                H25.8157.6540.9620.962
                下載: 導出CSV
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                        出版歷程
                        • 收稿日期:  2022-09-01
                        • 網絡出版日期:  2023-10-30
                        • 刊出日期:  2024-04-26

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